{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "14f74f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from itertools import product\n",
    "import os\n",
    "import sys\n",
    "from PIL import Image\n",
    "from scipy.optimize import minimize,linprog\n",
    "# import time\n",
    "# import seaborn as sns\n",
    "from sklearn.neighbors import KernelDensity\n",
    "# import pandas as pd\n",
    "from collections import Counter\n",
    "import numpy.linalg as la"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c16af61f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def file_extractor(dirname=\"images\"):\n",
    "    files = os.listdir(dirname)\n",
    "    scenes = []\n",
    "    for file in files:\n",
    "        if file == '.DS_Store':\n",
    "            continue\n",
    "        else:\n",
    "            scenes.append(os.path.join(dirname, file))\n",
    "    return scenes\n",
    "\n",
    "def image_extractor(scenes):\n",
    "    image_folder = []\n",
    "    for scene in scenes:\n",
    "        files = os.listdir(scene)\n",
    "        for file in files:\n",
    "            if file[-5:] != \".tiff\" or file[-7:] == \"_6.tiff\":\n",
    "                continue\n",
    "            else:\n",
    "                image_folder.append(os.path.join(scene, file))\n",
    "    return image_folder #returns a list of file paths to .tiff files in the specified directory given in file_extractor\n",
    "\n",
    "def im_distribution(images, num):\n",
    "    \"\"\"\n",
    "    Function that extracts tiff files from specific cameras and returns a list of all\n",
    "    the tiff files corresponding to that camera. i.e. all pictures labeled \"_7.tiff\" or otherwise\n",
    "    specified camera numbers.\n",
    "    \n",
    "    Parameters:\n",
    "        images (list): list of all tiff files, regardless of classification. This is NOT a list of directories but\n",
    "        of specific tiff files that can be opened right away. This is the list that we iterate through and \n",
    "        divide.\n",
    "        \n",
    "        num (str): a string designation for the camera number that we want to extract i.e. \"14\" for double digits\n",
    "        of \"_1\" for single digits.\n",
    "        \n",
    "    Returns:\n",
    "        tiff (list): A list of tiff files that have the specified designation from num. They are the files extracted\n",
    "        from the 'images' list that correspond to the given num.\n",
    "    \"\"\"\n",
    "    tiff = []\n",
    "    for im in images:\n",
    "        if im[-7:-5] == num:\n",
    "            tiff.append(im)\n",
    "    return tiff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53786325",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_pix(tiff_image_path, difference = True):\n",
    "    \"\"\"\n",
    "    This function predict the pixel values excluding the boundary.\n",
    "    Using the 4 neighbor pixel values and MSE to predict the next pixel value\n",
    "    (-1,1) (0,1) (1,1)  => relative position of the 4 other given values\n",
    "    (-1,0) (0,0)        => (0,0) is the one we want to predict\n",
    "    take the derivative of mean square error to solve for the system of equation \n",
    "    A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
    "    A @ [a, b, c] = [-z0+z2-z3, z0+z1+z2, -z0-z1-z2-z3] where z0 = (-1,1), z1 = (0,1), z2 = (1,1), z3 = (-1,0)\n",
    "    and the predicted pixel value is c.\n",
    "    \n",
    "    Input:\n",
    "    tiff_image_path (string): path to the tiff file\n",
    "    \n",
    "    Return:\n",
    "    image   ndarray(512 X 640): original image \n",
    "    predict ndarray(325380,): predicted image excluding the boundary\n",
    "    diff.   ndarray(325380,): IF difference = TRUE, difference between the min and max of four neighbors exclude the boundary\n",
    "                            ELSE: the residuals of the four nearest pixels to a fitted hyperplane\n",
    "    error   ndarray(325380,): difference between the original image and predicted image\n",
    "    A       ndarray(3 X 3): system of equation\n",
    "    \"\"\"\n",
    "    image_obj = Image.open(tiff_image_path)    #Open the image and read it as an Image object\n",
    "    image_array = np.array(image_obj)[1:,:].astype(int)    #Convert to an array, leaving out the first row because the first row is just housekeeping data\n",
    "    # image_array = image_array.astype(int) \n",
    "    A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation\n",
    "    # where z0 = (-1,1), z1 = (0,1), z2 = (1,1), z3 = (-1,0)\n",
    "    z0 = image_array[0:-2,0:-2]   # get all the first pixel for the entire image\n",
    "    z1 = image_array[0:-2,1:-1]   # get all the second pixel for the entire image\n",
    "    z2 = image_array[0:-2,2::]    # get all the third pixel for the entire image\n",
    "    z3 = image_array[1:-1,0:-2]   # get all the forth pixel for the entire image\n",
    "    \n",
    "    # calculate the out put of the system of equation\n",
    "    y0 = np.ravel(-z0+z2-z3)\n",
    "    y1 = np.ravel(z0+z1+z2)\n",
    "    y2 = np.ravel(-z0-z1-z2-z3)\n",
    "    y = np.vstack((y0,y1,y2))\n",
    "    \n",
    "    # use numpy solver to solve the system of equations all at once\n",
    "    #predict = np.floor(np.linalg.solve(A,y)[-1])\n",
    "    predict = np.round(np.round((np.linalg.solve(A,y)[-1]),1))\n",
    "    \n",
    "    #Matrix system of points that will be used to solve the least squares fitting hyperplane\n",
    "    points = np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])\n",
    "    \n",
    "    # flatten the neighbor pixlels and stack them together\n",
    "    z0 = np.ravel(z0)\n",
    "    z1 = np.ravel(z1)\n",
    "    z2 = np.ravel(z2)\n",
    "    z3 = np.ravel(z3)\n",
    "    neighbor = np.vstack((z0,z1,z2,z3)).T\n",
    "    \n",
    "    if difference:\n",
    "        # calculate the difference\n",
    "        diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n",
    "    \n",
    "    else:\n",
    "        #Compute the best fitting hyperplane using least squares\n",
    "        #The res is the residuals of the four points used to fit the hyperplane (summed distance of each of the \n",
    "        #points to the hyperplane), it is a measure of gradient\n",
    "        f, diff, rank, s = la.lstsq(points, neighbor.T, rcond=None)\n",
    "        diff = diff.astype(int)\n",
    "    \n",
    "    # calculate the error\n",
    "    error = np.ravel(image_array[1:-1,1:-1])-predict\n",
    "    \n",
    "    return image_array, predict, diff, error, A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b965751",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "this huffman encoding code is found online\n",
    "https://favtutor.com/blogs/huffman-coding\n",
    "\"\"\"\n",
    "\n",
    "class NodeTree(object):\n",
    "    def __init__(self, left=None, right=None):\n",
    "        self.left = left\n",
    "        self.right = right\n",
    "\n",
    "    def children(self):\n",
    "        return self.left, self.right\n",
    "\n",
    "    def __str__(self):\n",
    "        return self.left, self.right\n",
    "\n",
    "\n",
    "def huffman_code_tree(node, binString=''):\n",
    "    '''\n",
    "    Function to find Huffman Code\n",
    "    '''\n",
    "    if type(node) is str:\n",
    "        return {node: binString}\n",
    "    (l, r) = node.children()\n",
    "    d = dict()\n",
    "    d.update(huffman_code_tree(l, binString + '0'))\n",
    "    d.update(huffman_code_tree(r, binString + '1'))\n",
    "    return d\n",
    "\n",
    "\n",
    "def make_tree(nodes):\n",
    "    '''\n",
    "    Function to make tree\n",
    "    :param nodes: Nodes\n",
    "    :return: Root of the tree\n",
    "    '''\n",
    "    while len(nodes) > 1:\n",
    "        (key1, c1) = nodes[-1]\n",
    "        (key2, c2) = nodes[-2]\n",
    "        nodes = nodes[:-2]\n",
    "        node = NodeTree(key1, key2)\n",
    "        nodes.append((node, c1 + c2))\n",
    "        #reverse True, decending order\n",
    "\n",
    "        #There is a huge memory leak here, no idea how or why\n",
    "        nodes = sorted(nodes, key=lambda x: x[1], reverse=True)\n",
    "    return nodes[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b7561883",
   "metadata": {},
   "outputs": [],
   "source": [
    "def huffman(tiff_image_path, num_bins=4, difference = True):\n",
    "    \"\"\"\n",
    "    This function is used to encode the error based on the difference\n",
    "    and split the difference into different bins\n",
    "    \n",
    "    Input:\n",
    "    tiff_image_path     (string): path to the tiff file\n",
    "    num_bins            (int): number of bins\n",
    "    \n",
    "    Return:\n",
    "    huffman_encoding_list  list    (num_bins + 1): a list of dictionary\n",
    "    image_array            ndarray (512, 640): original image\n",
    "    new_error              ndarray (512, 640): error that includes the boundary\n",
    "    diff                   ndarray (510, 638): difference of min and max of the 4 neighbors\n",
    "    boundary               ndarray (2300,): the boundary values after subtracting the very first pixel value\n",
    "    predict                ndarray (325380,): the list of predicted values\n",
    "    bins                   list    (num_bins - 1,): a list of threshold to cut the bins\n",
    "    A                      ndarray (3 X 3): system of equation\n",
    "    \n",
    "    \"\"\"\n",
    "    # get the image_array, etc\n",
    "    image_array, predict, diff, error, A = predict_pix(tiff_image_path, difference)\n",
    "    \n",
    "    # calculate the number of points that will go in each bin\n",
    "    data_points_per_bin = diff.size // num_bins\n",
    "\n",
    "    # sort the difference and create the bins\n",
    "    sorted_diff = np.sort(diff.copy())\n",
    "    bins = [sorted_diff[i*data_points_per_bin] for i in range(1,num_bins)]\n",
    "    \n",
    "    # get the boundary \n",
    "    boundary = np.hstack((image_array[0,:],image_array[-1,:],image_array[1:-1,0],image_array[1:-1,-1]))\n",
    "    \n",
    "    # take the difference of the boundary with the very first pixel\n",
    "    boundary = boundary - image_array[0,0]\n",
    "    \n",
    "    #boundary is 1dim, so boundary[0] is just the first element\n",
    "    boundary[0] = image_array[0,0]\n",
    "    \n",
    "    # huffman encode the boundary\n",
    "    bound_vals_as_string = [str(i) for i in boundary]\n",
    "    freq = dict(Counter(bound_vals_as_string))\n",
    "    freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
    "    node = make_tree(freq)\n",
    "    huffman_encoding_dict = huffman_code_tree(node)\n",
    "    \n",
    "    # create a list of huffman table\n",
    "    huffman_encoding_list = [huffman_encoding_dict]\n",
    "    n = len(bins)\n",
    "    \n",
    "    # loop through different bins\n",
    "    for i in range (0,n):\n",
    "        # the first bin\n",
    "        if i == 0 :\n",
    "            # get the point within the bin and huffman huffman_encoding_dict\n",
    "            mask = diff <= bins[i]\n",
    "            line_as_string = [str(i) for i in error[mask].astype(int)]\n",
    "            freq = dict(Counter(line_as_string))\n",
    "            freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
    "            node = make_tree(freq)\n",
    "            huffman_encoding_dict = huffman_code_tree(node)\n",
    "            huffman_encoding_list.append(huffman_encoding_dict)\n",
    "            \n",
    "        # the middle bins\n",
    "        else:\n",
    "            # get the point within the bin and huffman huffman_encoding_dict\n",
    "            mask = diff > bins[i-1]\n",
    "            new_error = error[mask]\n",
    "            mask2 = diff[mask] <= bins[i]\n",
    "            line_as_string = [str(i) for i in new_error[mask2].astype(int)]\n",
    "            freq = dict(Counter(line_as_string))\n",
    "            freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
    "            node = make_tree(freq)\n",
    "            huffman_encoding_dict = huffman_code_tree(node)\n",
    "            huffman_encoding_list.append(huffman_encoding_dict)\n",
    "            \n",
    "    # the last bin       \n",
    "    # get the point within the bin and huffman huffman_encoding_dict\n",
    "    mask = diff > bins[-1]\n",
    "    line_as_string = [str(i) for i in error[mask].astype(int)]\n",
    "    freq = dict(Counter(line_as_string))\n",
    "    freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
    "    node = make_tree(freq)\n",
    "    huffman_encoding_dict = huffman_code_tree(node)\n",
    "    huffman_encoding_list.append(huffman_encoding_dict)\n",
    "\n",
    "    # create a error matrix that includes the boundary (used in encoding matrix)\n",
    "    new_error = np.copy(image_array)\n",
    "    new_error[1:-1,1:-1] = np.reshape(error,(510, 638))\n",
    "    keep = new_error[0,0]\n",
    "    new_error[0,:] = new_error[0,:] - keep\n",
    "    new_error[-1,:] = new_error[-1,:] - keep\n",
    "    new_error[1:-1,0] = new_error[1:-1,0] - keep\n",
    "    new_error[1:-1,-1] = new_error[1:-1,-1] - keep\n",
    "    new_error[0,0] = keep\n",
    "    # huffman_encoding_list = list(set(huffman_encoding_list))\n",
    "    diff = np.reshape(diff,(510,638))\n",
    "    # return the huffman dictionary\n",
    "    return huffman_encoding_list, image_array, new_error, diff, boundary, predict, bins, A\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2eb774d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encoder(error, list_dic, diff, bound, bins):\n",
    "    \"\"\"\n",
    "    This function encode the matrix with huffman coding tables\n",
    "    \n",
    "    Input:\n",
    "    error     (512, 640): a matrix with all the errors\n",
    "    list_dic  (num_dic + 1,): a list of huffman coding table \n",
    "    bound     (2300,): the boundary values after subtracting the very first pixel value\n",
    "    bins       (num_bins - 1,): a list of threshold to cut the bins\n",
    "    \n",
    "    Return:\n",
    "    encoded   (512, 640): encoded matrix\n",
    "    \"\"\"\n",
    "    # copy the error matrix (including the boundary)\n",
    "    encoded = np.copy(error).astype(int).astype(str).astype(object)\n",
    "    #diff = np.reshape(diff,(510,638))\n",
    "    # loop through all the pixel to encode\n",
    "    for i in range(encoded.shape[0]):\n",
    "        for j in range(encoded.shape[1]):\n",
    "            if i == 0 or i == encoded.shape[0]-1 or j == 0 or j == encoded.shape[1]-1:\n",
    "                encoded[i][j] = list_dic[0][encoded[i][j]]\n",
    "            elif diff[i-1][j-1] <= bins[0]:\n",
    "                encoded[i][j] = list_dic[1][encoded[i][j]]\n",
    "            elif diff[i-1][j-1] <= bins[1] and diff[i-1][j-1] > bins[0]:\n",
    "                encoded[i][j] = list_dic[2][encoded[i][j]]\n",
    "            elif diff[i-1][j-1] <= bins[2] and diff[i-1][j-1] > bins[1]:\n",
    "                encoded[i][j] = list_dic[3][encoded[i][j]]\n",
    "            else: \n",
    "                encoded[i][j] = list_dic[4][encoded[i][j]]\n",
    "\n",
    "    return encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8eeb40d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decoder(A, encoded_matrix, list_dic, bins, use_diff):\n",
    "    \"\"\"\n",
    "    This function decodes the encoded_matrix.\n",
    "    Input:\n",
    "    A               (3 X 3): system of equation\n",
    "    list_dic        (num_dic + 1,): a list of huffman coding table \n",
    "    encoded_matrix  (512, 640): encoded matrix\n",
    "    bins            (num_bins - 1,): a list of threshold to cut the bins\n",
    "    \n",
    "    Return:\n",
    "    decode_matrix   (512, 640): decoded matrix\n",
    "    \"\"\"\n",
    "    # change the dictionary back to list\n",
    "    # !!!!!WARNING!!!! has to change this part, eveytime you change the number of bins\n",
    "    the_keys0 = list(list_dic[0].keys())\n",
    "    the_values0 = list(list_dic[0].values())\n",
    "    \n",
    "    the_keys1 = list(list_dic[1].keys())\n",
    "    the_values1 = list(list_dic[1].values())\n",
    "    \n",
    "    the_keys2 = list(list_dic[2].keys())\n",
    "    the_values2 = list(list_dic[2].values())\n",
    "    \n",
    "    the_keys3 = list(list_dic[3].keys())\n",
    "    the_values3 = list(list_dic[3].values())\n",
    "    \n",
    "    the_keys4 = list(list_dic[4].keys())\n",
    "    the_values4 = list(list_dic[4].values())\n",
    "    \n",
    "    #Matrix system of points that will be used to solve the least squares fitting hyperplane\n",
    "    points = np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])\n",
    "    \n",
    "    decode_matrix = np.zeros((512,640))\n",
    "    # loop through all the element in the matrix\n",
    "    for i in range(decode_matrix.shape[0]):\n",
    "        for j in range(decode_matrix.shape[1]):\n",
    "            # if it's the very first pixel on the image\n",
    "            if i == 0 and j == 0:\n",
    "                decode_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])])\n",
    "                print(encoded_matrix[i][j])\n",
    "                print(the_values0.index(encoded_matrix[i,j]))\n",
    "                print(int(the_keys0[the_values0.index(encoded_matrix[i,j])]))\n",
    "            # if it's on the boundary (any of the 4 edges)\n",
    "            elif i == 0 or i == decode_matrix.shape[0]-1 or j == 0 or j == decode_matrix.shape[1]-1:\n",
    "                decode_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])]) + decode_matrix[0][0]\n",
    "            # if not the boundary\n",
    "            else:\n",
    "                # predict the image with the known pixel value\n",
    "                z0 = decode_matrix[i-1][j-1]\n",
    "                z1 = decode_matrix[i-1][j]\n",
    "                z2 = decode_matrix[i-1][j+1]\n",
    "                z3 = decode_matrix[i][j-1]\n",
    "                y0 = int(-z0+z2-z3)\n",
    "                y1 = int(z0+z1+z2)\n",
    "                y2 = int(-z0-z1-z2-z3)\n",
    "                y = np.vstack((y0,y1,y2))\n",
    "                if use_diff:\n",
    "                    difference = max(z0,z1,z2,z3) - min(z0,z1,z2,z3)\n",
    "                else:\n",
    "                    \n",
    "                    f, difference, rank, s = la.lstsq(points, [z0,z1,z2,z3], rcond=None) \n",
    "                    difference = difference.astype(int)\n",
    "                    \n",
    "                predict = np.round(np.round(np.linalg.solve(A,y)[-1][0],1))\n",
    "                \n",
    "                # add on the difference by searching the dictionary\n",
    "                # !!!!!WARNING!!!! has to change this part, eveytime you change the number of bins\n",
    "                if difference <= bins[0]:\n",
    "                    decode_matrix[i][j] = int(the_keys1[the_values1.index(encoded_matrix[i,j])]) + int(predict)\n",
    "                elif difference <= bins[1] and difference > bins[0]:\n",
    "                    decode_matrix[i][j] = int(the_keys2[the_values2.index(encoded_matrix[i,j])]) + int(predict)\n",
    "                elif difference <= bins[2] and difference > bins[1]:\n",
    "                    decode_matrix[i][j] = int(the_keys3[the_values3.index(encoded_matrix[i,j])]) + int(predict)\n",
    "                else:\n",
    "                    decode_matrix[i][j] = int(the_keys4[the_values4.index(encoded_matrix[i,j])]) + int(predict)\n",
    "                \n",
    "                \n",
    "    return decode_matrix.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f959fe93",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compress_rate(image_array, new_error, diff, bound, huffman_encoding_list, bins):\n",
    "    '''\n",
    "    This function is used to calculate the compression rate.\n",
    "    Input:\n",
    "    image_array      (512, 640): original_core image\n",
    "    new_error  (512, 640): error that includes the boundary\n",
    "    diff       (510, 638): difference of min and max of the 4 neighbors\n",
    "    bound      (2300,): the boundary values after subtracting the very first pixel value\n",
    "    huffman_encoding_list   (num_dic + 1,): a list of huffman coding table \n",
    "    bins       (num_bins - 1,): a list of threshold to cut the bins\n",
    "    \n",
    "    Return:\n",
    "    compression rate\n",
    "    '''\n",
    "    # the bits for the original image\n",
    "    o_len = 0\n",
    "    # the bits for the compressed image\n",
    "    c_len = 0\n",
    "    # initializing the varible \n",
    "    \n",
    "    #this was unused\n",
    "    # im = np.reshape(image,(512, 640))\n",
    "    \n",
    "    real_boundary = np.hstack((image_array[0,:],image_array[-1,:],image_array[1:-1,0],image_array[1:-1,-1]))\n",
    "    #Bryce's notes: Why are they all reshaped?\n",
    "    original_core = image_array[1:-1,1:-1].reshape(-1)\n",
    "    diff = diff.reshape(-1)\n",
    "    error = new_error[1:-1,1:-1].reshape(-1)\n",
    "    \n",
    "    # calculate the bit for boundary\n",
    "    for i in range(0,len(bound)):\n",
    "        o_len += len(bin(real_boundary[i])[2:])\n",
    "        c_len += len(huffman_encoding_list[0][str(bound[i])])\n",
    "    \n",
    "    # calculate the bit for the pixels inside the boundary\n",
    "    for i in range(0,len(original_core)):\n",
    "\n",
    "        # for the original image\n",
    "        o_len += len(bin(original_core[i])[2:])\n",
    "        \n",
    "        # check the difference and find the coresponding huffman table\n",
    "        # !!!!!WARNING!!!! has to change this part, eveytime you change the number of bins\n",
    "        if diff[i] <= bins[0]:\n",
    "            c_len += len(huffman_encoding_list[1][str(int(error[i]))])\n",
    "            \n",
    "        elif diff[i] <= bins[1] and diff[i] > bins[0]:\n",
    "            c_len += len(huffman_encoding_list[2][str(int(error[i]))])\n",
    "            \n",
    "        elif diff[i] <= bins[2] and diff[i] > bins[1]:\n",
    "            c_len += len(huffman_encoding_list[3][str(int(error[i]))])\n",
    "\n",
    "        else: \n",
    "            c_len += len(huffman_encoding_list[4][str(int(error[i]))])\n",
    "            \n",
    "    return c_len/o_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3e0e9742",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11100100000\n",
      "499\n",
      "22275\n",
      "True\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "scenes = file_extractor()\n",
    "images = image_extractor(scenes)\n",
    "list_dic, image, new_error, diff, bound, predict, bins, A = huffman(images[0], 4, False)\n",
    "encoded_matrix = encoder(new_error, list_dic, diff, bound, bins)\n",
    "reconstruct_image = decoder(A, encoded_matrix, list_dic, bins, False)\n",
    "print(np.allclose(image, reconstruct_image))\n",
    "print(len(list_dic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "004e8ba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4232928466796875"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compress_rate(image, new_error, diff, bound, list_dic, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a282f9e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2621552\n",
      "2621552\n"
     ]
    }
   ],
   "source": [
    "print(sys.getsizeof(encoded_matrix))\n",
    "print(sys.getsizeof(reconstruct_image))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7efe26b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b46343b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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